The Integrated Cortical Model (ICM) is based upon several models of the mammalian visual cortex and produces pulse images over several iterations. These pulse images tend to isolate segments, edges, and textures that are inherent in the input image. To create a texture recognition engine the pulse spectrum of individual pixels are collected and used to develop a recognition library. Recognition is performed by comparing pulse spectra of unclassified regions of images with the known regions. Because signatures are smaller than images, signature-based computation is quite efficient and parasites can be recognized quickly. The precision of this method depends on the representative of signatures and classification. Our experiment results support the theoretical findings and show perspectives of practical applications of ICM-based method. The advantage of ICM method is using signatures to represent objects. ICM can extract the internal features of objects and represent them with signatures. Signature classification is critical for the precision of recognition.